Dear Editor,The rapid development of barley genomics research in recent years has greatly enhanced our understanding of the molecular regulatory mechanisms underlying the complex characters(Jiang et al.,2025).However,...Dear Editor,The rapid development of barley genomics research in recent years has greatly enhanced our understanding of the molecular regulatory mechanisms underlying the complex characters(Jiang et al.,2025).However,a huge challenge has also been posed for researchers to deal with the dramatically increasing amount of multi-omics data.展开更多
Hybrid breeding is widely acknowledged as the most effective method for increasing crop yield,particularly in maize and rice.However,a major challenge in hybrid breeding is the selection of desirable combinations from...Hybrid breeding is widely acknowledged as the most effective method for increasing crop yield,particularly in maize and rice.However,a major challenge in hybrid breeding is the selection of desirable combinations from the vast pool of potential crosses.Genomic selection(GS)has emerged as a powerful tool to tackle this challenge,but its success in practical breeding depends on prediction accuracy.Several strategies have been explored to enhance prediction accuracy for complex traits,such as the incorporation of functional markers and multi-omics data.Metabolome-wide association studies(MWAS)help to identify metabolites that are closely linked to phenotypes,known as metabolic markers.However,the use of preselected metabolic markers from parental lines to predict hybrid performance has not yet been explored.In this study,we developed a novel approach called metabolic marker-assisted genomic prediction(MM_GP),which incorporates significant metabolites identified from MWAS into GS models to improve the accuracy of genomic hybrid prediction.In maize and rice hybrid populations,MM_GP outperformed genomic prediction(GP)for all traits,regardless of the method used(genomic best linear unbiased prediction or eXtreme gradient boosting).On average,MM_GP demonstrated 4.6%and 13.6%higher predictive abilities than GP for maize and rice,respectively.MM_GP could also match or even surpass the predictive ability of M_GP(integrated genomic-metabolomic prediction)for most traits.In maize,the integration of only six metabolic markers significantly associated with multiple traits resulted in 5.0%and 3.1%higher average predictive ability compared with GP and M_GP,respectively.With advances in high-throughput metabolomics technologies and prediction models,this approach holds great promise for revolutionizing genomic hybrid breeding by enhancing its accuracy and efficiency.展开更多
Enviromics refers to the characterization of micro-and macroenvironments based on large-scale environmental datasets.By providing genotypic recommendations with predictive extrapolation at a site-specific level,enviro...Enviromics refers to the characterization of micro-and macroenvironments based on large-scale environmental datasets.By providing genotypic recommendations with predictive extrapolation at a site-specific level,enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate.Enviromics-based integration of statistics,envirotyping(i.e.,determining environmental factors),and remote sensing could help unravel the complex interplay of genetics,environment,and management.To support this goal,exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops.Already,informatics management platforms aggregate diverse environmental datasets obtained using optical,thermal,radar,and light detection and ranging(LiDAR)sensors that capture detailed information about vegetation,surface structure,and terrain.This wealth of information,coupled with freely available climate data,fuels innovative enviromics research.While enviromics holds immense potential for breeding,a few obstacles remain,such as the need for(1)integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data;(2)state-of-the-art AI models for data integration,simulation,and prediction;(3)cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders;and(4)collaboration and data sharing among farmers,breeders,physiologists,geoinformatics experts,and programmers across research institutions.Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.展开更多
基金supported by the National Natural Science Foundation of China(32171917)the Zhejiang Science and Technology Major Program on Agricultural New Variety Breeding(2021C02064-3)+1 种基金the Shenzhen Science and Technology Program(KQTD20230301092839007)the China Agricultural Research System(CARS-05).
文摘Dear Editor,The rapid development of barley genomics research in recent years has greatly enhanced our understanding of the molecular regulatory mechanisms underlying the complex characters(Jiang et al.,2025).However,a huge challenge has also been posed for researchers to deal with the dramatically increasing amount of multi-omics data.
基金supported by grants from the National Key Research and Development Program of China(2023YFD1202200)the National Natural Science Foundation of China(32170636,32061143030,32261143462,32100448,32070558)+6 种基金the Seed Industry Revitalization Project of Jiangsu Province(JBGS[2021]009)the Key Research and Development Program of Jiangsu Province(BE2022343,BE2023336)Jiangsu Province Agricultural Science and Technology Independent Innovation(CX(21)1003)the Shenzhen Science and Technology Program(KQTD202303010928390070)the Hebei Science and Tech-nology Program(215A7612D)the Shanghai Agricultural Science and Technology Innovation Program(T2023204)the Provincial Technology Innovation Program of Shandong,China,Qing Lan Project of Jiangsu Province,Yangzhou University High-end Talent Support Program,and the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD).
文摘Hybrid breeding is widely acknowledged as the most effective method for increasing crop yield,particularly in maize and rice.However,a major challenge in hybrid breeding is the selection of desirable combinations from the vast pool of potential crosses.Genomic selection(GS)has emerged as a powerful tool to tackle this challenge,but its success in practical breeding depends on prediction accuracy.Several strategies have been explored to enhance prediction accuracy for complex traits,such as the incorporation of functional markers and multi-omics data.Metabolome-wide association studies(MWAS)help to identify metabolites that are closely linked to phenotypes,known as metabolic markers.However,the use of preselected metabolic markers from parental lines to predict hybrid performance has not yet been explored.In this study,we developed a novel approach called metabolic marker-assisted genomic prediction(MM_GP),which incorporates significant metabolites identified from MWAS into GS models to improve the accuracy of genomic hybrid prediction.In maize and rice hybrid populations,MM_GP outperformed genomic prediction(GP)for all traits,regardless of the method used(genomic best linear unbiased prediction or eXtreme gradient boosting).On average,MM_GP demonstrated 4.6%and 13.6%higher predictive abilities than GP for maize and rice,respectively.MM_GP could also match or even surpass the predictive ability of M_GP(integrated genomic-metabolomic prediction)for most traits.In maize,the integration of only six metabolic markers significantly associated with multiple traits resulted in 5.0%and 3.1%higher average predictive ability compared with GP and M_GP,respectively.With advances in high-throughput metabolomics technologies and prediction models,this approach holds great promise for revolutionizing genomic hybrid breeding by enhancing its accuracy and efficiency.
基金R.T.R.,L.L.P.,and G.E.M.thank the Brazilian agencies Coordenac¸ao de Aperfeic¸oamento de Pessoal de Nıvel Superior(CAPES)and Conselho Nacional de Desenvolvimento Cientıfico e Tecnologico(CNPq)for the financial support,which was instrumental in the successful execution of this project.L.H.was supported through an ARC Future Fellowship(FT220100350)from the Australian Research Council.C.H.A.was supported by The University of Colorado Boulder Grand ChallengeCIRES Earth Lab.Y.X.was supported by the Agricultural Science and Technology Innovation Program(ASTIP)of the Chinese Academy of Agricultural Sciences,Shenzhen Science and Technology Program(KQTD202303010928390070)Hebei Science and Technology Program(215A7612D),and the Provincial Technology Innovation Program of Shandong,China.
文摘Enviromics refers to the characterization of micro-and macroenvironments based on large-scale environmental datasets.By providing genotypic recommendations with predictive extrapolation at a site-specific level,enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate.Enviromics-based integration of statistics,envirotyping(i.e.,determining environmental factors),and remote sensing could help unravel the complex interplay of genetics,environment,and management.To support this goal,exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops.Already,informatics management platforms aggregate diverse environmental datasets obtained using optical,thermal,radar,and light detection and ranging(LiDAR)sensors that capture detailed information about vegetation,surface structure,and terrain.This wealth of information,coupled with freely available climate data,fuels innovative enviromics research.While enviromics holds immense potential for breeding,a few obstacles remain,such as the need for(1)integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data;(2)state-of-the-art AI models for data integration,simulation,and prediction;(3)cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders;and(4)collaboration and data sharing among farmers,breeders,physiologists,geoinformatics experts,and programmers across research institutions.Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.
